Overview

Dataset statistics

Number of variables19
Number of observations5872
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory871.8 KiB
Average record size in memory152.0 B

Variable types

Categorical6
Numeric13

Warnings

track has a high cardinality: 5619 distinct values High cardinality
artist has a high cardinality: 2920 distinct values High cardinality
uri has a high cardinality: 5855 distinct values High cardinality
danceability is highly correlated with valenceHigh correlation
energy is highly correlated with loudness and 1 other fieldsHigh correlation
loudness is highly correlated with energy and 1 other fieldsHigh correlation
acousticness is highly correlated with energy and 1 other fieldsHigh correlation
valence is highly correlated with danceabilityHigh correlation
duration_ms is highly correlated with sectionsHigh correlation
sections is highly correlated with duration_msHigh correlation
danceability is highly correlated with valenceHigh correlation
energy is highly correlated with loudness and 1 other fieldsHigh correlation
loudness is highly correlated with energyHigh correlation
acousticness is highly correlated with energyHigh correlation
instrumentalness is highly correlated with targetHigh correlation
valence is highly correlated with danceabilityHigh correlation
duration_ms is highly correlated with sectionsHigh correlation
sections is highly correlated with duration_msHigh correlation
target is highly correlated with instrumentalnessHigh correlation
duration_ms is highly correlated with sectionsHigh correlation
sections is highly correlated with duration_msHigh correlation
acousticness is highly correlated with energy and 1 other fieldsHigh correlation
valence is highly correlated with energy and 1 other fieldsHigh correlation
energy is highly correlated with acousticness and 4 other fieldsHigh correlation
target is highly correlated with energy and 2 other fieldsHigh correlation
danceability is highly correlated with valence and 2 other fieldsHigh correlation
loudness is highly correlated with acousticness and 1 other fieldsHigh correlation
sections is highly correlated with duration_msHigh correlation
instrumentalness is highly correlated with targetHigh correlation
duration_ms is highly correlated with sectionsHigh correlation
track is uniformly distributed Uniform
uri is uniformly distributed Uniform
target is uniformly distributed Uniform
key has 632 (10.8%) zeros Zeros
instrumentalness has 2206 (37.6%) zeros Zeros

Reproduction

Analysis started2021-07-31 18:10:57.271984
Analysis finished2021-07-31 18:11:14.445854
Duration17.17 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

track
Categorical

HIGH CARDINALITY
UNIFORM

Distinct5619
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Memory size46.0 KiB
Breathe
 
5
The One
 
4
Beautiful
 
4
Closer
 
4
Angel
 
4
Other values (5614)
5851 

Length

Max length124
Median length14
Mean length17.44346049
Min length1

Characters and Unicode

Total characters102428
Distinct characters162
Distinct categories14 ?
Distinct scripts6 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5401 ?
Unique (%)92.0%

Sample

1st rowLucky Man
2nd rowOn The Hotline
3rd rowClouds Of Dementia
4th rowHeavy Metal, Raise Hell!
5th rowI Got A Feelin'

Common Values

ValueCountFrequency (%)
Breathe5
 
0.1%
The One4
 
0.1%
Beautiful4
 
0.1%
Closer4
 
0.1%
Angel4
 
0.1%
Forever4
 
0.1%
Girlfriend4
 
0.1%
Personality Crisis3
 
0.1%
You Raise Me Up3
 
0.1%
All Summer Long3
 
0.1%
Other values (5609)5834
99.4%

Length

2021-07-31T13:11:14.661797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the679
 
3.5%
540
 
2.8%
you352
 
1.8%
i300
 
1.5%
of287
 
1.5%
a249
 
1.3%
me238
 
1.2%
in215
 
1.1%
to193
 
1.0%
love172
 
0.9%
Other values (5597)16267
83.5%

Most occurring characters

ValueCountFrequency (%)
13620
 
13.3%
e9090
 
8.9%
o6422
 
6.3%
a6308
 
6.2%
n5173
 
5.1%
i4988
 
4.9%
t4664
 
4.6%
r4645
 
4.5%
s3330
 
3.3%
l3158
 
3.1%
Other values (152)41030
40.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter66861
65.3%
Uppercase Letter18107
 
17.7%
Space Separator13620
 
13.3%
Other Punctuation1722
 
1.7%
Decimal Number702
 
0.7%
Dash Punctuation567
 
0.6%
Open Punctuation391
 
0.4%
Close Punctuation390
 
0.4%
Other Letter40
 
< 0.1%
Final Punctuation8
 
< 0.1%
Other values (4)20
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e9090
13.6%
o6422
 
9.6%
a6308
 
9.4%
n5173
 
7.7%
i4988
 
7.5%
t4664
 
7.0%
r4645
 
6.9%
s3330
 
5.0%
l3158
 
4.7%
h2798
 
4.2%
Other values (37)16285
24.4%
Other Letter
ValueCountFrequency (%)
3
 
7.5%
2
 
5.0%
1
 
2.5%
1
 
2.5%
1
 
2.5%
1
 
2.5%
1
 
2.5%
1
 
2.5%
1
 
2.5%
1
 
2.5%
Other values (27)27
67.5%
Uppercase Letter
ValueCountFrequency (%)
T1675
 
9.3%
S1401
 
7.7%
M1318
 
7.3%
A1141
 
6.3%
I1130
 
6.2%
B1068
 
5.9%
L991
 
5.5%
W941
 
5.2%
D894
 
4.9%
R856
 
4.7%
Other values (22)6692
37.0%
Other Punctuation
ValueCountFrequency (%)
'684
39.7%
.324
18.8%
,188
 
10.9%
:183
 
10.6%
"94
 
5.5%
&53
 
3.1%
/52
 
3.0%
!46
 
2.7%
?35
 
2.0%
;34
 
2.0%
Other values (6)29
 
1.7%
Decimal Number
ValueCountFrequency (%)
1135
19.2%
0130
18.5%
2129
18.4%
960
8.5%
556
8.0%
454
 
7.7%
344
 
6.3%
739
 
5.6%
629
 
4.1%
826
 
3.7%
Open Punctuation
ValueCountFrequency (%)
(378
96.7%
[11
 
2.8%
2
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
-564
99.5%
2
 
0.4%
1
 
0.2%
Nonspacing Mark
ValueCountFrequency (%)
2
50.0%
1
25.0%
1
25.0%
Math Symbol
ValueCountFrequency (%)
~3
60.0%
=1
 
20.0%
+1
 
20.0%
Close Punctuation
ValueCountFrequency (%)
)379
97.2%
]11
 
2.8%
Final Punctuation
ValueCountFrequency (%)
7
87.5%
1
 
12.5%
Modifier Symbol
ValueCountFrequency (%)
´4
66.7%
`2
33.3%
Space Separator
ValueCountFrequency (%)
13620
100.0%
Connector Punctuation
ValueCountFrequency (%)
_5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin84968
83.0%
Common17416
 
17.0%
Han19
 
< 0.1%
Thai17
 
< 0.1%
Hiragana4
 
< 0.1%
Katakana4
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e9090
 
10.7%
o6422
 
7.6%
a6308
 
7.4%
n5173
 
6.1%
i4988
 
5.9%
t4664
 
5.5%
r4645
 
5.5%
s3330
 
3.9%
l3158
 
3.7%
h2798
 
3.3%
Other values (69)34392
40.5%
Common
ValueCountFrequency (%)
13620
78.2%
'684
 
3.9%
-564
 
3.2%
)379
 
2.2%
(378
 
2.2%
.324
 
1.9%
,188
 
1.1%
:183
 
1.1%
1135
 
0.8%
0130
 
0.7%
Other values (33)831
 
4.8%
Han
ValueCountFrequency (%)
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
Other values (9)9
47.4%
Thai
ValueCountFrequency (%)
3
17.6%
2
11.8%
2
11.8%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
Other values (3)3
17.6%
Hiragana
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Katakana
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII102212
99.8%
Latin 1 Sup156
 
0.2%
CJK19
 
< 0.1%
Thai17
 
< 0.1%
Punctuation14
 
< 0.1%
Hiragana4
 
< 0.1%
Katakana4
 
< 0.1%
Latin Ext A2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13620
 
13.3%
e9090
 
8.9%
o6422
 
6.3%
a6308
 
6.2%
n5173
 
5.1%
i4988
 
4.9%
t4664
 
4.6%
r4645
 
4.5%
s3330
 
3.3%
l3158
 
3.1%
Other values (77)40814
39.9%
Latin 1 Sup
ValueCountFrequency (%)
é42
26.9%
á20
12.8%
ä15
 
9.6%
ö9
 
5.8%
å8
 
5.1%
ó8
 
5.1%
è7
 
4.5%
ê5
 
3.2%
´4
 
2.6%
à3
 
1.9%
Other values (18)35
22.4%
Thai
ValueCountFrequency (%)
3
17.6%
2
11.8%
2
11.8%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
Other values (3)3
17.6%
Latin Ext A
ValueCountFrequency (%)
ű2
100.0%
Punctuation
ValueCountFrequency (%)
7
50.0%
2
 
14.3%
2
 
14.3%
1
 
7.1%
1
 
7.1%
1
 
7.1%
CJK
ValueCountFrequency (%)
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
Other values (9)9
47.4%
Hiragana
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Katakana
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

artist
Categorical

HIGH CARDINALITY

Distinct2920
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Memory size46.0 KiB
Toby Keith
 
27
Rascal Flatts
 
24
Tim McGraw
 
24
Iron Maiden
 
23
Kenny Chesney
 
23
Other values (2915)
5751 

Length

Max length88
Median length12
Mean length13.97905313
Min length2

Characters and Unicode

Total characters82085
Distinct characters98
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1949 ?
Unique (%)33.2%

Sample

1st rowMontgomery Gentry
2nd rowPretty Ricky
3rd rowCandlemass
4th rowZwartketterij
5th rowBilly Currington

Common Values

ValueCountFrequency (%)
Toby Keith27
 
0.5%
Rascal Flatts24
 
0.4%
Tim McGraw24
 
0.4%
Iron Maiden23
 
0.4%
Kenny Chesney23
 
0.4%
George Strait22
 
0.4%
Keith Urban20
 
0.3%
Brad Paisley20
 
0.3%
Harry Belafonte19
 
0.3%
Alan Jackson19
 
0.3%
Other values (2910)5651
96.2%

Length

2021-07-31T13:11:14.931259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
featuring557
 
4.2%
the397
 
3.0%
254
 
1.9%
of117
 
0.9%
lil99
 
0.7%
keith56
 
0.4%
wayne51
 
0.4%
joe50
 
0.4%
kelly47
 
0.4%
j46
 
0.3%
Other values (3673)11708
87.5%

Most occurring characters

ValueCountFrequency (%)
7510
 
9.1%
e7133
 
8.7%
a6849
 
8.3%
n5123
 
6.2%
i5112
 
6.2%
r4709
 
5.7%
o4132
 
5.0%
t3495
 
4.3%
l3299
 
4.0%
s3113
 
3.8%
Other values (88)31610
38.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter59337
72.3%
Uppercase Letter13932
 
17.0%
Space Separator7510
 
9.1%
Other Punctuation840
 
1.0%
Decimal Number260
 
0.3%
Dash Punctuation184
 
0.2%
Open Punctuation6
 
< 0.1%
Close Punctuation6
 
< 0.1%
Currency Symbol5
 
< 0.1%
Math Symbol4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e7133
12.0%
a6849
11.5%
n5123
 
8.6%
i5112
 
8.6%
r4709
 
7.9%
o4132
 
7.0%
t3495
 
5.9%
l3299
 
5.6%
s3113
 
5.2%
h2281
 
3.8%
Other values (32)14091
23.7%
Uppercase Letter
ValueCountFrequency (%)
S1106
 
7.9%
T1101
 
7.9%
B1037
 
7.4%
M997
 
7.2%
F987
 
7.1%
C900
 
6.5%
A821
 
5.9%
D768
 
5.5%
J747
 
5.4%
L648
 
4.7%
Other values (20)4820
34.6%
Decimal Number
ValueCountFrequency (%)
251
19.6%
051
19.6%
540
15.4%
136
13.8%
334
13.1%
421
8.1%
610
 
3.8%
710
 
3.8%
86
 
2.3%
91
 
0.4%
Other Punctuation
ValueCountFrequency (%)
.349
41.5%
&249
29.6%
'119
 
14.2%
,62
 
7.4%
!31
 
3.7%
"18
 
2.1%
:7
 
0.8%
/4
 
0.5%
?1
 
0.1%
Space Separator
ValueCountFrequency (%)
7510
100.0%
Dash Punctuation
ValueCountFrequency (%)
-184
100.0%
Currency Symbol
ValueCountFrequency (%)
$5
100.0%
Open Punctuation
ValueCountFrequency (%)
(6
100.0%
Math Symbol
ValueCountFrequency (%)
+4
100.0%
Close Punctuation
ValueCountFrequency (%)
)6
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin73269
89.3%
Common8816
 
10.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e7133
 
9.7%
a6849
 
9.3%
n5123
 
7.0%
i5112
 
7.0%
r4709
 
6.4%
o4132
 
5.6%
t3495
 
4.8%
l3299
 
4.5%
s3113
 
4.2%
h2281
 
3.1%
Other values (62)28023
38.2%
Common
ValueCountFrequency (%)
7510
85.2%
.349
 
4.0%
&249
 
2.8%
-184
 
2.1%
'119
 
1.3%
,62
 
0.7%
251
 
0.6%
051
 
0.6%
540
 
0.5%
136
 
0.4%
Other values (16)165
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII82002
99.9%
Latin 1 Sup81
 
0.1%
Latin Ext A1
 
< 0.1%
Punctuation1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7510
 
9.2%
e7133
 
8.7%
a6849
 
8.4%
n5123
 
6.2%
i5112
 
6.2%
r4709
 
5.7%
o4132
 
5.0%
t3495
 
4.3%
l3299
 
4.0%
s3113
 
3.8%
Other values (67)31527
38.4%
Latin 1 Sup
ValueCountFrequency (%)
ö17
21.0%
é12
14.8%
ó8
9.9%
á6
 
7.4%
ú6
 
7.4%
ü5
 
6.2%
ä4
 
4.9%
Ó3
 
3.7%
å3
 
3.7%
Á3
 
3.7%
Other values (9)14
17.3%
Latin Ext A
ValueCountFrequency (%)
ő1
100.0%
Punctuation
ValueCountFrequency (%)
1
100.0%

uri
Categorical

HIGH CARDINALITY
UNIFORM

Distinct5855
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size46.0 KiB
spotify:track:5YSW99IUAIiAj243jF7PBO
 
2
spotify:track:1mJ05BN0So26a14iib85aI
 
2
spotify:track:49bUJjrC16NgnrgGS75Yan
 
2
spotify:track:77FULy278MulVvGWS8BfK7
 
2
spotify:track:3XVBdLihbNbxUwZosxcGuJ
 
2
Other values (5850)
5862 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters211392
Distinct characters63
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5838 ?
Unique (%)99.4%

Sample

1st rowspotify:track:4GiXBCUF7H6YfNQsnBRIzl
2nd rowspotify:track:1zyqZONW985Cs4osz9wlsu
3rd rowspotify:track:6cHZf7RbxXCKwEkgAZT4mY
4th rowspotify:track:2IjBPp2vMeX7LggzRN3iSX
5th rowspotify:track:1tF370eYXUcWwkIvaq3IGz

Common Values

ValueCountFrequency (%)
spotify:track:5YSW99IUAIiAj243jF7PBO2
 
< 0.1%
spotify:track:1mJ05BN0So26a14iib85aI2
 
< 0.1%
spotify:track:49bUJjrC16NgnrgGS75Yan2
 
< 0.1%
spotify:track:77FULy278MulVvGWS8BfK72
 
< 0.1%
spotify:track:3XVBdLihbNbxUwZosxcGuJ2
 
< 0.1%
spotify:track:7Kpqjspw4Y7HrvItIRcBiW2
 
< 0.1%
spotify:track:4TbNLKRLKlxZDlS0pu7Lsy2
 
< 0.1%
spotify:track:6NvRxjfYkkT2SpirAlmsjH2
 
< 0.1%
spotify:track:0t9Jd84JnsV8HRMaQzHUom2
 
< 0.1%
spotify:track:1qHRFZE8qykNXYZadzmi1m2
 
< 0.1%
Other values (5845)5852
99.7%

Length

2021-07-31T13:11:15.594480image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
spotify:track:561jh07mf1jhuk7klaef0s2
 
< 0.1%
spotify:track:7kpqjspw4y7hrvitircbiw2
 
< 0.1%
spotify:track:49bujjrc16ngnrggs75yan2
 
< 0.1%
spotify:track:4ggyigsxhpypsgittwlwlt2
 
< 0.1%
spotify:track:6pwzcktrkrwbupzy8rlcop2
 
< 0.1%
spotify:track:0t9jd84jnsv8hrmaqzhuom2
 
< 0.1%
spotify:track:5ysw99iuaiiaj243jf7pbo2
 
< 0.1%
spotify:track:3xvbdlihbnbxuwzosxcguj2
 
< 0.1%
spotify:track:77fuly278mulvvgws8bfk72
 
< 0.1%
spotify:track:7ukcscnxuo3mww6lowbjw12
 
< 0.1%
Other values (5845)5852
99.7%

Most occurring characters

ValueCountFrequency (%)
t13729
 
6.5%
:11744
 
5.6%
i7927
 
3.7%
a7854
 
3.7%
o7852
 
3.7%
s7847
 
3.7%
k7830
 
3.7%
c7827
 
3.7%
f7817
 
3.7%
p7808
 
3.7%
Other values (53)123157
58.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter122051
57.7%
Uppercase Letter51715
24.5%
Decimal Number25882
 
12.2%
Other Punctuation11744
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t13729
 
11.2%
i7927
 
6.5%
a7854
 
6.4%
o7852
 
6.4%
s7847
 
6.4%
k7830
 
6.4%
c7827
 
6.4%
f7817
 
6.4%
p7808
 
6.4%
y7808
 
6.4%
Other values (16)37752
30.9%
Uppercase Letter
ValueCountFrequency (%)
A2067
 
4.0%
S2066
 
4.0%
V2052
 
4.0%
B2028
 
3.9%
D2027
 
3.9%
N2026
 
3.9%
U2017
 
3.9%
F2017
 
3.9%
E2016
 
3.9%
T2012
 
3.9%
Other values (16)31387
60.7%
Decimal Number
ValueCountFrequency (%)
62839
11.0%
42808
10.8%
02766
10.7%
32747
10.6%
12733
10.6%
52727
10.5%
22696
10.4%
72646
10.2%
81999
7.7%
91921
7.4%
Other Punctuation
ValueCountFrequency (%)
:11744
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin173766
82.2%
Common37626
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t13729
 
7.9%
i7927
 
4.6%
a7854
 
4.5%
o7852
 
4.5%
s7847
 
4.5%
k7830
 
4.5%
c7827
 
4.5%
f7817
 
4.5%
p7808
 
4.5%
y7808
 
4.5%
Other values (42)89467
51.5%
Common
ValueCountFrequency (%)
:11744
31.2%
62839
 
7.5%
42808
 
7.5%
02766
 
7.4%
32747
 
7.3%
12733
 
7.3%
52727
 
7.2%
22696
 
7.2%
72646
 
7.0%
81999
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII211392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t13729
 
6.5%
:11744
 
5.6%
i7927
 
3.7%
a7854
 
3.7%
o7852
 
3.7%
s7847
 
3.7%
k7830
 
3.7%
c7827
 
3.7%
f7817
 
3.7%
p7808
 
3.7%
Other values (53)123157
58.3%

danceability
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct881
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5429307732
Minimum0.0588
Maximum0.986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.0 KiB
2021-07-31T13:11:15.748114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.0588
5-th percentile0.199
Q10.416
median0.556
Q30.681
95-th percentile0.83845
Maximum0.986
Range0.9272
Interquartile range (IQR)0.265

Descriptive statistics

Standard deviation0.1900027071
Coefficient of variation (CV)0.3499575204
Kurtosis-0.5004118539
Mean0.5429307732
Median Absolute Deviation (MAD)0.131
Skewness-0.2505687214
Sum3188.0895
Variance0.03610102871
MonotonicityNot monotonic
2021-07-31T13:11:15.932887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.56823
 
0.4%
0.49920
 
0.3%
0.5120
 
0.3%
0.55719
 
0.3%
0.719
 
0.3%
0.59919
 
0.3%
0.64819
 
0.3%
0.5819
 
0.3%
0.64719
 
0.3%
0.54919
 
0.3%
Other values (871)5676
96.7%
ValueCountFrequency (%)
0.05881
< 0.1%
0.05971
< 0.1%
0.061
< 0.1%
0.06091
< 0.1%
0.0611
< 0.1%
0.0641
< 0.1%
0.0651
< 0.1%
0.06551
< 0.1%
0.06841
< 0.1%
0.06981
< 0.1%
ValueCountFrequency (%)
0.9861
< 0.1%
0.9781
< 0.1%
0.9741
< 0.1%
0.9681
< 0.1%
0.9671
< 0.1%
0.9631
< 0.1%
0.9621
< 0.1%
0.9611
< 0.1%
0.9561
< 0.1%
0.9551
< 0.1%

energy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1010
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6945108549
Minimum0.000348
Maximum0.999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.0 KiB
2021-07-31T13:11:16.111258image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.000348
5-th percentile0.17665
Q10.567
median0.744
Q30.885
95-th percentile0.976
Maximum0.999
Range0.998652
Interquartile range (IQR)0.318

Descriptive statistics

Standard deviation0.2377917942
Coefficient of variation (CV)0.3423874408
Kurtosis0.3946398955
Mean0.6945108549
Median Absolute Deviation (MAD)0.155
Skewness-0.978129495
Sum4078.16774
Variance0.05654493738
MonotonicityNot monotonic
2021-07-31T13:11:16.296142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.91323
 
0.4%
0.94722
 
0.4%
0.922
 
0.4%
0.95921
 
0.4%
0.9420
 
0.3%
0.95120
 
0.3%
0.93420
 
0.3%
0.98720
 
0.3%
0.93620
 
0.3%
0.87119
 
0.3%
Other values (1000)5665
96.5%
ValueCountFrequency (%)
0.0003481
< 0.1%
0.0009821
< 0.1%
0.00111
< 0.1%
0.001311
< 0.1%
0.00151
< 0.1%
0.001831
< 0.1%
0.002611
< 0.1%
0.002671
< 0.1%
0.002811
< 0.1%
0.002831
< 0.1%
ValueCountFrequency (%)
0.99911
0.2%
0.9988
0.1%
0.9979
0.2%
0.99613
0.2%
0.99517
0.3%
0.99414
0.2%
0.99313
0.2%
0.9929
0.2%
0.99114
0.2%
0.997
0.1%

key
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.276396458
Minimum0
Maximum11
Zeros632
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size46.0 KiB
2021-07-31T13:11:16.465397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.564152999
Coefficient of variation (CV)0.6754899917
Kurtosis-1.295041904
Mean5.276396458
Median Absolute Deviation (MAD)3
Skewness0.01652731677
Sum30983
Variance12.7031866
MonotonicityNot monotonic
2021-07-31T13:11:16.534399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7637
10.8%
0632
10.8%
1623
10.6%
2620
10.6%
9558
9.5%
4479
8.2%
11470
8.0%
5455
7.7%
6414
7.1%
8407
6.9%
Other values (2)577
9.8%
ValueCountFrequency (%)
0632
10.8%
1623
10.6%
2620
10.6%
3176
 
3.0%
4479
8.2%
5455
7.7%
6414
7.1%
7637
10.8%
8407
6.9%
9558
9.5%
ValueCountFrequency (%)
11470
8.0%
10401
6.8%
9558
9.5%
8407
6.9%
7637
10.8%
6414
7.1%
5455
7.7%
4479
8.2%
3176
 
3.0%
2620
10.6%

loudness
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4420
Distinct (%)75.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.449258004
Minimum-47.327
Maximum1.137
Zeros0
Zeros (%)0.0%
Negative5871
Negative (%)> 99.9%
Memory size46.0 KiB
2021-07-31T13:11:16.634251image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-47.327
5-th percentile-18.06465
Q1-8.315
median-6.0415
Q3-4.5625
95-th percentile-2.9922
Maximum1.137
Range48.464
Interquartile range (IQR)3.7525

Descriptive statistics

Standard deviation5.102543302
Coefficient of variation (CV)-0.6849733624
Kurtosis9.872633769
Mean-7.449258004
Median Absolute Deviation (MAD)1.7365
Skewness-2.749146287
Sum-43742.043
Variance26.03594815
MonotonicityNot monotonic
2021-07-31T13:11:16.728004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4.6155
 
0.1%
-4.7535
 
0.1%
-3.7985
 
0.1%
-5.3795
 
0.1%
-8.5275
 
0.1%
-5.2185
 
0.1%
-4.0725
 
0.1%
-3.9085
 
0.1%
-4.5495
 
0.1%
-4.6784
 
0.1%
Other values (4410)5823
99.2%
ValueCountFrequency (%)
-47.3271
< 0.1%
-44.3471
< 0.1%
-43.1781
< 0.1%
-41.0861
< 0.1%
-39.9851
< 0.1%
-39.9821
< 0.1%
-39.7791
< 0.1%
-39.6141
< 0.1%
-38.9991
< 0.1%
-38.9171
< 0.1%
ValueCountFrequency (%)
1.1371
< 0.1%
-0.2961
< 0.1%
-0.3661
< 0.1%
-0.5591
< 0.1%
-0.7941
< 0.1%
-0.8641
< 0.1%
-0.8731
< 0.1%
-0.8841
< 0.1%
-0.9491
< 0.1%
-0.9561
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.0 KiB
1
3788 
0
2084 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5872
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
13788
64.5%
02084
35.5%

Length

2021-07-31T13:11:16.912887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-31T13:11:16.966296image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
13788
64.5%
02084
35.5%

Most occurring characters

ValueCountFrequency (%)
13788
64.5%
02084
35.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5872
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
13788
64.5%
02084
35.5%

Most occurring scripts

ValueCountFrequency (%)
Common5872
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
13788
64.5%
02084
35.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII5872
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13788
64.5%
02084
35.5%

speechiness
Real number (ℝ≥0)

Distinct1077
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09235953678
Minimum0.0224
Maximum0.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.0 KiB
2021-07-31T13:11:17.035300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.0224
5-th percentile0.0277
Q10.036175
median0.0527
Q30.107
95-th percentile0.302
Maximum0.95
Range0.9276
Interquartile range (IQR)0.070825

Descriptive statistics

Standard deviation0.0949972309
Coefficient of variation (CV)1.02855898
Kurtosis10.91819042
Mean0.09235953678
Median Absolute Deviation (MAD)0.0212
Skewness2.744591585
Sum542.3352
Variance0.009024473879
MonotonicityNot monotonic
2021-07-31T13:11:17.147801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.031723
 
0.4%
0.033623
 
0.4%
0.10722
 
0.4%
0.10221
 
0.4%
0.02921
 
0.4%
0.033721
 
0.4%
0.033421
 
0.4%
0.031621
 
0.4%
0.1220
 
0.3%
0.029820
 
0.3%
Other values (1067)5659
96.4%
ValueCountFrequency (%)
0.02241
 
< 0.1%
0.02271
 
< 0.1%
0.02281
 
< 0.1%
0.02292
< 0.1%
0.02324
0.1%
0.02332
< 0.1%
0.02341
 
< 0.1%
0.02351
 
< 0.1%
0.02361
 
< 0.1%
0.02371
 
< 0.1%
ValueCountFrequency (%)
0.951
< 0.1%
0.9431
< 0.1%
0.9411
< 0.1%
0.9141
< 0.1%
0.8561
< 0.1%
0.8221
< 0.1%
0.7861
< 0.1%
0.7661
< 0.1%
0.7581
< 0.1%
0.71
< 0.1%

acousticness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2725
Distinct (%)46.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2143736073
Minimum0
Maximum0.996
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size46.0 KiB
2021-07-31T13:11:17.297164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.1855 × 10-5
Q10.0045525
median0.0603
Q30.312
95-th percentile0.942
Maximum0.996
Range0.996
Interquartile range (IQR)0.3074475

Descriptive statistics

Standard deviation0.2965107361
Coefficient of variation (CV)1.383149446
Kurtosis0.8179623454
Mean0.2143736073
Median Absolute Deviation (MAD)0.0601065
Skewness1.444147585
Sum1258.801822
Variance0.08791861661
MonotonicityNot monotonic
2021-07-31T13:11:17.425730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.99214
 
0.2%
0.10313
 
0.2%
0.99513
 
0.2%
0.99113
 
0.2%
0.9913
 
0.2%
0.99412
 
0.2%
0.010812
 
0.2%
0.98912
 
0.2%
0.21612
 
0.2%
0.20911
 
0.2%
Other values (2715)5747
97.9%
ValueCountFrequency (%)
02
< 0.1%
1.01 × 10-61
< 0.1%
1.04 × 10-61
< 0.1%
1.05 × 10-61
< 0.1%
1.06 × 10-61
< 0.1%
1.08 × 10-61
< 0.1%
1.11 × 10-61
< 0.1%
1.12 × 10-61
< 0.1%
1.13 × 10-61
< 0.1%
1.14 × 10-61
< 0.1%
ValueCountFrequency (%)
0.9966
0.1%
0.99513
0.2%
0.99412
0.2%
0.99310
0.2%
0.99214
0.2%
0.99113
0.2%
0.9913
0.2%
0.98912
0.2%
0.9886
0.1%
0.98711
0.2%

instrumentalness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2296
Distinct (%)39.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1509171542
Minimum0
Maximum0.998
Zeros2206
Zeros (%)37.6%
Negative0
Negative (%)0.0%
Memory size46.0 KiB
2021-07-31T13:11:17.536513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.185 × 10-5
Q30.047275
95-th percentile0.893
Maximum0.998
Range0.998
Interquartile range (IQR)0.047275

Descriptive statistics

Standard deviation0.3014518284
Coefficient of variation (CV)1.997465629
Kurtosis1.419082246
Mean0.1509171542
Median Absolute Deviation (MAD)2.185 × 10-5
Skewness1.766149508
Sum886.1855296
Variance0.09087320487
MonotonicityNot monotonic
2021-07-31T13:11:17.652047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02206
37.6%
0.82610
 
0.2%
0.9259
 
0.2%
0.9149
 
0.2%
0.8839
 
0.2%
0.8778
 
0.1%
0.8938
 
0.1%
0.918
 
0.1%
0.8617
 
0.1%
1.22 × 10-57
 
0.1%
Other values (2286)3591
61.2%
ValueCountFrequency (%)
02206
37.6%
1.01 × 10-63
 
0.1%
1.02 × 10-64
 
0.1%
1.03 × 10-61
 
< 0.1%
1.04 × 10-61
 
< 0.1%
1.06 × 10-62
 
< 0.1%
1.07 × 10-61
 
< 0.1%
1.08 × 10-63
 
0.1%
1.09 × 10-62
 
< 0.1%
1.1 × 10-63
 
0.1%
ValueCountFrequency (%)
0.9982
< 0.1%
0.9892
< 0.1%
0.9881
< 0.1%
0.9851
< 0.1%
0.9832
< 0.1%
0.9822
< 0.1%
0.9811
< 0.1%
0.9792
< 0.1%
0.9781
< 0.1%
0.9761
< 0.1%

liveness
Real number (ℝ≥0)

Distinct1200
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1961408038
Minimum0.0193
Maximum0.987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.0 KiB
2021-07-31T13:11:17.767935image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.0193
5-th percentile0.0554
Q10.0937
median0.131
Q30.263
95-th percentile0.53845
Maximum0.987
Range0.9677
Interquartile range (IQR)0.1693

Descriptive statistics

Standard deviation0.161964941
Coefficient of variation (CV)0.8257585258
Kurtosis5.255135271
Mean0.1961408038
Median Absolute Deviation (MAD)0.054
Skewness2.114726153
Sum1151.7388
Variance0.02623264212
MonotonicityNot monotonic
2021-07-31T13:11:17.867831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.10763
 
1.1%
0.10159
 
1.0%
0.10955
 
0.9%
0.11154
 
0.9%
0.10854
 
0.9%
0.10653
 
0.9%
0.10451
 
0.9%
0.10547
 
0.8%
0.11446
 
0.8%
0.11346
 
0.8%
Other values (1190)5344
91.0%
ValueCountFrequency (%)
0.01931
< 0.1%
0.02091
< 0.1%
0.02141
< 0.1%
0.02161
< 0.1%
0.02241
< 0.1%
0.02331
< 0.1%
0.02342
< 0.1%
0.02351
< 0.1%
0.02451
< 0.1%
0.02461
< 0.1%
ValueCountFrequency (%)
0.9871
< 0.1%
0.9851
< 0.1%
0.9821
< 0.1%
0.9761
< 0.1%
0.9741
< 0.1%
0.9731
< 0.1%
0.9721
< 0.1%
0.9711
< 0.1%
0.9652
< 0.1%
0.9642
< 0.1%

valence
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1158
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4822621424
Minimum0
Maximum0.982
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size46.0 KiB
2021-07-31T13:11:17.983713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.064055
Q10.278
median0.486
Q30.687
95-th percentile0.901
Maximum0.982
Range0.982
Interquartile range (IQR)0.409

Descriptive statistics

Standard deviation0.2545668891
Coefficient of variation (CV)0.5278599889
Kurtosis-0.9924514658
Mean0.4822621424
Median Absolute Deviation (MAD)0.204
Skewness0.01733850927
Sum2831.8433
Variance0.06480430104
MonotonicityNot monotonic
2021-07-31T13:11:18.099591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.96118
 
0.3%
0.96217
 
0.3%
0.54315
 
0.3%
0.50215
 
0.3%
0.45515
 
0.3%
0.54814
 
0.2%
0.32814
 
0.2%
0.55614
 
0.2%
0.61114
 
0.2%
0.50414
 
0.2%
Other values (1148)5722
97.4%
ValueCountFrequency (%)
01
< 0.1%
0.01131
< 0.1%
0.01721
< 0.1%
0.01931
< 0.1%
0.02072
< 0.1%
0.02091
< 0.1%
0.02381
< 0.1%
0.02411
< 0.1%
0.02462
< 0.1%
0.02471
< 0.1%
ValueCountFrequency (%)
0.9821
 
< 0.1%
0.9791
 
< 0.1%
0.9781
 
< 0.1%
0.9761
 
< 0.1%
0.9731
 
< 0.1%
0.9723
 
0.1%
0.9712
 
< 0.1%
0.973
 
0.1%
0.9697
0.1%
0.96711
0.2%

tempo
Real number (ℝ≥0)

Distinct5445
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.6130182
Minimum46.755
Maximum213.233
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.0 KiB
2021-07-31T13:11:18.215464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum46.755
5-th percentile78.1071
Q196.984
median119.999
Q3141.49525
95-th percentile175.72925
Maximum213.233
Range166.478
Interquartile range (IQR)44.51125

Descriptive statistics

Standard deviation30.17988535
Coefficient of variation (CV)0.2481632788
Kurtosis-0.5220185827
Mean121.6130182
Median Absolute Deviation (MAD)22.444
Skewness0.3802370482
Sum714111.643
Variance910.8254795
MonotonicityNot monotonic
2021-07-31T13:11:18.328446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119.9875
 
0.1%
119.9995
 
0.1%
99.9714
 
0.1%
119.9884
 
0.1%
109.9984
 
0.1%
94.9974
 
0.1%
130.0224
 
0.1%
125.044
 
0.1%
99.9854
 
0.1%
953
 
0.1%
Other values (5435)5831
99.3%
ValueCountFrequency (%)
46.7551
< 0.1%
47.371
< 0.1%
49.8751
< 0.1%
56.0281
< 0.1%
56.791
< 0.1%
58.0991
< 0.1%
58.51
< 0.1%
59.321
< 0.1%
59.3591
< 0.1%
59.9721
< 0.1%
ValueCountFrequency (%)
213.2331
< 0.1%
210.8571
< 0.1%
209.9051
< 0.1%
209.8191
< 0.1%
208.0781
< 0.1%
207.6761
< 0.1%
207.6731
< 0.1%
207.5751
< 0.1%
207.0211
< 0.1%
206.3091
< 0.1%

duration_ms
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4928
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258170.6282
Minimum15920
Maximum4170227
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.0 KiB
2021-07-31T13:11:18.453416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum15920
5-th percentile145500.7
Q1206813
median238006.5
Q3279160
95-th percentile417653.45
Maximum4170227
Range4154307
Interquartile range (IQR)72347

Descriptive statistics

Standard deviation139534.1212
Coefficient of variation (CV)0.5404724858
Kurtosis264.8009259
Mean258170.6282
Median Absolute Deviation (MAD)34760
Skewness11.88399445
Sum1515977929
Variance1.946977098 × 1010
MonotonicityNot monotonic
2021-07-31T13:11:18.553670image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2366005
 
0.1%
1952805
 
0.1%
2138005
 
0.1%
2438675
 
0.1%
2325335
 
0.1%
2476004
 
0.1%
2442274
 
0.1%
2164274
 
0.1%
2930534
 
0.1%
2093334
 
0.1%
Other values (4918)5827
99.2%
ValueCountFrequency (%)
159201
< 0.1%
231331
< 0.1%
241071
< 0.1%
258801
< 0.1%
275331
< 0.1%
289201
< 0.1%
315601
< 0.1%
317871
< 0.1%
345601
< 0.1%
383481
< 0.1%
ValueCountFrequency (%)
41702271
< 0.1%
38163731
< 0.1%
37914801
< 0.1%
21043471
< 0.1%
17611071
< 0.1%
16611201
< 0.1%
15550931
< 0.1%
15140001
< 0.1%
13697731
< 0.1%
13691731
< 0.1%

time_signature
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size46.0 KiB
4
5308 
3
 
426
5
 
84
1
 
53
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5872
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
45308
90.4%
3426
 
7.3%
584
 
1.4%
153
 
0.9%
01
 
< 0.1%

Length

2021-07-31T13:11:18.738823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-31T13:11:18.798580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
45308
90.4%
3426
 
7.3%
584
 
1.4%
153
 
0.9%
01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
45308
90.4%
3426
 
7.3%
584
 
1.4%
153
 
0.9%
01
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5872
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
45308
90.4%
3426
 
7.3%
584
 
1.4%
153
 
0.9%
01
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common5872
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
45308
90.4%
3426
 
7.3%
584
 
1.4%
153
 
0.9%
01
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5872
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
45308
90.4%
3426
 
7.3%
584
 
1.4%
153
 
0.9%
01
 
< 0.1%

chorus_hit
Real number (ℝ≥0)

Distinct5821
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.73429538
Minimum0
Maximum262.6154
Zeros14
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size46.0 KiB
2021-07-31T13:11:18.876647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.7031865
Q127.5075075
median36.03716
Q347.88957
95-th percentile77.826186
Maximum262.6154
Range262.6154
Interquartile range (IQR)20.3820625

Descriptive statistics

Standard deviation20.2456367
Coefficient of variation (CV)0.4970169856
Kurtosis9.92645646
Mean40.73429538
Median Absolute Deviation (MAD)9.711455
Skewness2.268137025
Sum239191.7825
Variance409.8858053
MonotonicityNot monotonic
2021-07-31T13:11:18.965611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
014
 
0.2%
28.358022
 
< 0.1%
46.837342
 
< 0.1%
46.09712
 
< 0.1%
18.637352
 
< 0.1%
39.062012
 
< 0.1%
43.543352
 
< 0.1%
21.908052
 
< 0.1%
29.831222
 
< 0.1%
65.412712
 
< 0.1%
Other values (5811)5840
99.5%
ValueCountFrequency (%)
014
0.2%
4.985521
 
< 0.1%
6.735881
 
< 0.1%
7.11361
 
< 0.1%
7.679781
 
< 0.1%
7.800221
 
< 0.1%
7.91031
 
< 0.1%
7.920421
 
< 0.1%
8.366811
 
< 0.1%
8.980631
 
< 0.1%
ValueCountFrequency (%)
262.61541
< 0.1%
219.636241
< 0.1%
206.92731
< 0.1%
187.569011
< 0.1%
181.377831
< 0.1%
181.276141
< 0.1%
180.257751
< 0.1%
168.813061
< 0.1%
167.820061
< 0.1%
166.15221
< 0.1%

sections
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct58
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.05688011
Minimum1
Maximum169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.0 KiB
2021-07-31T13:11:19.055118image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q19
median10
Q312
95-th percentile17
Maximum169
Range168
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.75818572
Coefficient of variation (CV)0.5207785255
Kurtosis240.0272296
Mean11.05688011
Median Absolute Deviation (MAD)2
Skewness11.06296718
Sum64926
Variance33.15670278
MonotonicityNot monotonic
2021-07-31T13:11:19.125462image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10882
15.0%
9808
13.8%
11744
12.7%
12645
11.0%
8563
9.6%
13433
7.4%
7370
6.3%
14282
 
4.8%
6211
 
3.6%
15188
 
3.2%
Other values (48)746
12.7%
ValueCountFrequency (%)
11
 
< 0.1%
213
 
0.2%
337
 
0.6%
461
 
1.0%
5124
 
2.1%
6211
 
3.6%
7370
6.3%
8563
9.6%
9808
13.8%
10882
15.0%
ValueCountFrequency (%)
1691
< 0.1%
1591
< 0.1%
1451
< 0.1%
971
< 0.1%
711
< 0.1%
691
< 0.1%
611
< 0.1%
591
< 0.1%
572
< 0.1%
562
< 0.1%

target
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.0 KiB
0
2936 
1
2936 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5872
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
02936
50.0%
12936
50.0%

Length

2021-07-31T13:11:19.303026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-31T13:11:19.340367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
02936
50.0%
12936
50.0%

Most occurring characters

ValueCountFrequency (%)
12936
50.0%
02936
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5872
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
12936
50.0%
02936
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common5872
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
12936
50.0%
02936
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5872
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12936
50.0%
02936
50.0%

Interactions

2021-07-31T13:10:59.563254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:10:59.657968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:10:59.727137image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:10:59.813490image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:10:59.893455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:10:59.968658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:00.050567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:00.131122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:00.213308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:00.287884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:00.371076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:00.444128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:00.525351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:00.600211image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:00.682734image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:00.764681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:00.846515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:00.918175image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:01.000543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:01.080317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:01.153998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:01.235837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:01.317640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:01.399322image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:01.481029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:01.562826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:01.644549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:01.726087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:01.805806image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:01.879349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:01.950811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:02.124647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:02.196636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:02.279613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:02.358249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:02.440006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:02.514087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:02.586544image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:02.669662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:02.752717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:02.825401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:02.898336image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:02.979278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:03.054326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:03.127184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:03.200021image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:03.283197image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:03.354321image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:03.436865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:03.518258image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:03.589902image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:03.669599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:03.744501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:03.828896image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:03.908897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:03.986247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:04.061311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:04.137004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:04.216152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:04.289393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:04.370778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:04.452282image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:04.533578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:04.614424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:04.693883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:04.775441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:04.848760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:04.930337image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:05.021660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:05.111252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:05.196884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:05.272787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:05.354081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:05.547405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:05.628825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:05.710304image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:05.792296image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:05.873735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:05.955200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:06.036496image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:06.117965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:06.236552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:06.344591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:06.429297image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:06.513590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:06.599823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:06.682962image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:06.767586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:06.867839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:06.945960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:07.030593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:07.137777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:07.210052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:07.284118image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:07.368654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:07.446774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:07.560186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:07.643903image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:07.733017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:07.834087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:07.917021image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:07.998213image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:08.121660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:08.203097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:08.298899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:08.380439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:08.462344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:08.541486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:08.614820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:08.696259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:08.775392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:08.858706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:08.940190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:09.021464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:09.112683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:09.193695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:09.293448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:09.395675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:09.477005image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:09.561612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:09.646171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:09.746425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:09.815424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:10.039935image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:10.121296image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:10.212262image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:10.293849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:10.375224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:10.465534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:10.547845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:10.639342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:10.712765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:10.794003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:10.873077image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:10.946286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:11.027709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:11.106995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:11.188634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:11.263004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:11.345440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:11.433404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:11.519681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:11.600943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:11.682314image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:11.768647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:11.851423image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:11.935557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:12.013679image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:12.082905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:12.172315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:12.248419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:12.330012image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:12.411588image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:12.494317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:12.576110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:12.657956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:12.739808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:12.830671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:12.915720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:12.998371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:13.088515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:13.173047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:13.264699image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:13.347597image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:13.440504image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:13.522200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:13.614660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:13.696758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-31T13:11:13.786665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-07-31T13:11:19.402870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-07-31T13:11:19.596078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-07-31T13:11:19.772725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-07-31T13:11:20.179622image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-07-31T13:11:20.345433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-07-31T13:11:13.976234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-07-31T13:11:14.314726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

trackartisturidanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mstime_signaturechorus_hitsectionstarget
0Lucky ManMontgomery Gentryspotify:track:4GiXBCUF7H6YfNQsnBRIzl0.5780.4714-7.27010.02890.3680000.000000.15900.532133.061196707430.88059131
1On The HotlinePretty Rickyspotify:track:1zyqZONW985Cs4osz9wlsu0.7040.85410-5.47700.18300.0185000.000000.14800.68892.988242587441.51106101
2Clouds Of DementiaCandlemassspotify:track:6cHZf7RbxXCKwEkgAZT4mY0.1620.8369-3.00910.04730.0001110.004570.17400.30086.964338893465.32887130
3Heavy Metal, Raise Hell!Zwartketterijspotify:track:2IjBPp2vMeX7LggzRN3iSX0.1880.9944-3.74510.16600.0000070.078400.19200.333148.440255667458.5952890
4I Got A Feelin'Billy Curringtonspotify:track:1tF370eYXUcWwkIvaq3IGz0.6300.7642-4.35310.02750.3630000.000000.12500.631112.098193760422.62384101
5Dantzig StationState Of Artspotify:track:5Z3nrC0JbJmXaOGiXTuNFk0.7260.83711-7.22300.09650.3730000.268000.13600.969135.347192720428.29051100
6DivorcedBlacklistedspotify:track:0iAdSLiQBIizTAiLUP7p5E0.3650.9221-2.64410.07100.0028500.000000.32100.29077.25089427445.7720240
7Where I Come FromAlan Jacksonspotify:track:6ej1QJ8eIYmhsyTlvgDajy0.7260.63111-8.13600.03340.2200000.000000.19300.746124.711239240435.59732101
8Nothin' To Die ForTim McGrawspotify:track:3lRSz6HujrSy9b3LXg2Kq90.4810.78610-5.65410.02880.0538000.000000.07590.389153.105253640419.65701111
9I Want to Know Your PlansSay Anythingspotify:track:3pjnCLIHbRczUjenWOEo560.6470.3247-9.67910.03770.3540000.000000.11500.344124.213314286332.66343160

Last rows

trackartisturidanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mstime_signaturechorus_hitsectionstarget
5862Postcards From HellThe Wood Brothersspotify:track:72i7dwVrHdfDnr3qmINh5U0.4650.24209-10.46010.02980.9550000.0223000.12600.315102.812284862435.07251140
5863(You Drive Me) CrazyBritney Spearsspotify:track:1DSJNBNhGZCigg9ll5VeZv0.7480.93900-4.28800.03410.0534000.0000000.32000.960104.001198067419.2942691
5864The AnthemGood Charlottespotify:track:0BRHnOFm6sjxN1i9LJrUDu0.4940.93901-3.12710.12600.0066600.0000000.13900.893177.751175093415.89251111
5865I CanNasspotify:track:2NPxL1QqPrD1a7OLHjVcAP0.8370.88506-3.91400.18200.1030000.0000000.06660.69495.313253720417.67790121
5866Shindo-kakuASIAN KUNG-FU GENERATIONspotify:track:1lzVhHihby5uHDwml2ApDr0.3230.95309-4.27810.06170.0000160.3770000.05240.576184.884147200421.9897580
5867Summer RainCarl Thomasspotify:track:0NBHHa8wwwmBnn3aAzX5wJ0.6670.62706-10.48800.06540.0972000.0000520.11100.784186.081232560440.87045101
5868And ICiaraspotify:track:1Jp9n1uHB72CfK31j4mEPh0.6910.38906-10.12510.06530.2550000.0000000.09810.437122.219233840481.7773571
5869Mass in B minor BWV 232, Missa: Duetto - Christe eleison - soprano/mezzo sopranoJohann Sebastian Bachspotify:track:4NIOi1ImMfdufRTsgoKjbD0.2970.07732-23.83910.06200.9510000.0002170.12100.40175.916275560437.51903110
5870LoogThe Cleanspotify:track:2Qyj2nUdm8y37TCCzDasFn0.3900.60107-8.23600.02910.0313000.9470000.11900.439116.122223627439.84092110
5871What The World NeedsWynonnaspotify:track:38Q6YF0TO7E4Dq6K0zdVUk0.5390.74000-5.56600.04900.1940000.0000000.07600.675170.054217160424.95471131